class_docstr: A block transforming a common nn.Module to a shape-normalized Maze perception block.
  
  :param in_keys: Keys identifying the input tensors.
  :param out_keys: Keys identifying the output tensors.
  :param in_shapes: List of input shapes.
  :param in_num_dims: Required number of dimensions for corresponding input.
  :param out_num_dims: Required number of dimensions for corresponding output.
  :param net: An nn.Module PyTorch net (the forward method of which must accept a Tensor input dict as parameter
              and must return a Tensor output dict)
code: def __repr__(self):
      txt = f"{TorchModelBlock.__name__}"
      txt += f"\n\tOut Shapes: {self.out_shapes()}"
      return txt
  
file_path: maze/maze/perception/blocks/general/torch_model_block.py
imports: from typing import Union, List, Sequence, Dict
  import torch
  import torch.nn
  from maze.core.annotations import override
  from maze.perception.blocks.shape_normalization import ShapeNormalizationBlock
label_desc: Use image node_id 3 for calling the TorchModelBlock obj's underlying member method code with example usage: obj.__repr__() and returns: txt